Haemophilus parahaemolyticus septic shock after aspiration pneumonia, France

Diagnostic strategy of metagenomic next-generation sequencing for gram negative bacteria in respiratory infections

OBJECTIVE

This study aims to identify the most effective diagnostic method for distinguishing pathogenic and non-pathogenic Gram-negative bacteria (GNB) in suspected pneumonia cases using metagenomic next-generation sequencing (mNGS) on bronchoalveolar lavage fluid (BALF) samples.

METHODS

The effectiveness of mNGS was assessed on BALF samples collected from 583 patients, and the results were compared with those from microbiological culture and final clinical diagnosis. Three interpretational approaches were evaluated for diagnostic accuracy.

RESULTS

mNGS outperformed culture significantly. Among the interpretational approaches, Clinical Interpretation (CI) demonstrated the best diagnostic performance with a sensitivity of 87.3%, specificity of 100%, positive predictive value of 100%, and negative predictive value of 98.3%. CI's specificity was significantly higher than Simple Interpretation (SI) at 37.9%. Additionally, CI excluded some microorganisms identified as putative pathogens by SI, including Haemophilus parainfluenzae, Haemophilus parahaemolyticus, and Klebsiella aerogenes.

CONCLUSION

Proper interpretation of mNGS data is crucial for accurately diagnosing respiratory infections caused by GNB. CI is recommended for this purpose.

Development of Single-Cell Transcriptomics and Its Application in COVID-19

Over the last three years, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related health crisis has claimed over six million lives and caused USD 12 trillion losses to the global economy. SARS-CoV-2 continuously mutates and evolves with a high basic reproduction number (R0), resulting in a variety of clinical manifestations ranging from asymptomatic infection to acute respiratory distress syndrome (ARDS) and even death. To gain a better understanding of coronavirus disease 2019 (COVID-19), it is critical to investigate the components that cause various clinical manifestations. Single-cell sequencing has substantial advantages in terms of identifying differentially expressed genes among individual cells, which can provide a better understanding of the various physiological and pathological processes. This article reviewed the use of single-cell transcriptomics in COVID-19 research, examined the immune response disparities generated by SARS-CoV-2, and offered insights regarding how to improve COVID-19 diagnosis and treatment plans.

Insights into the Profile of the Human Expiratory Microbiota and Its Associations with Indoor Microbiotas

Microorganisms residing in the human respiratory tract can be exhaled, and they constitute a part of environmental microbiotas. However, the expiratory microbiota community and its associations with environmental microbiotas remain poorly understood. Here, expiratory bacteria and fungi and the corresponding microbiotas from the living environments were characterized by DNA amplicon sequencing of residents' exhaled breath condensate (EBC) and environmental samples collected from 14 residences in Nanjing, China. The microbiotas of EBC samples, with a substantial heterogeneity, were found to be as diverse as those of skin, floor dust, and airborne microbiotas. Model fitting results demonstrated the role of stochastic processes in the assembly of the expiratory microbiota. Using a fast expectation-maximization algorithm, microbial community analysis revealed that expiratory microbiotas were differentially associated with other types of microbiotas in a type-dependent and residence-specific manner. Importantly, the expiratory bacteria showed a composition similarity with airborne bacteria in the bathroom and kitchen environments with an average of 12.60%, while the expiratory fungi showed a 53.99% composition similarity with the floor dust fungi. These differential patterns indicate different relationships between expiratory microbiotas and the airborne microbiotas and floor dust microbiotas. The results here illustrated for the first time the associations between expiratory microbiotas and indoor microbiotas, showing a potential microbial exchange between the respiratory tract and indoor environment. Thus, improved hygiene and ventilation practices can be implemented to optimize the indoor microbial exposome, especially in indoor bathrooms and kitchens.

PathogenTrack and Yeskit: tools for identifying intracellular pathogens from single-cell RNA-sequencing datasets as illustrated by application to COVID-19

Pathogenic microbes can induce cellular dysfunction, immune response, and cause infectious disease and other diseases including cancers. However, the cellular distributions of pathogens and their impact on host cells remain rarely explored due to the limited methods. Taking advantage of single-cell RNA-sequencing (scRNA-seq) analysis, we can assess the transcriptomic features at the single-cell level. Still, the tools used to interpret pathogens (such as viruses, bacteria, and fungi) at the single-cell level remain to be explored. Here, we introduced PathogenTrack, a python-based computational pipeline that uses unmapped scRNA-seq data to identify intracellular pathogens at the single-cell level. In addition, we established an R package named Yeskit to import, integrate, analyze, and interpret pathogen abundance and transcriptomic features in host cells. Robustness of these tools has been tested on various real and simulated scRNA-seq datasets. PathogenTrack is competitive to the state-of-the-art tools such as Viral-Track, and the first tools for identifying bacteria at the single-cell level. Using the raw data of bronchoalveolar lavage fluid samples (BALF) from COVID-19 patients in the SRA database, we found the SARS-CoV-2 virus exists in multiple cell types including epithelial cells and macrophages. SARS-CoV-2-positive neutrophils showed increased expression of genes related to type I interferon pathway and antigen presenting module. Additionally, we observed the Haemophilus parahaemolyticus in some macrophage and epithelial cells, indicating a co-infection of the bacterium in some severe cases of COVID-19. The PathogenTrack pipeline and the Yeskit package are publicly available at GitHub.

Microbial contamination profile change over a 4-year period in nonoperated cleft soft palate

AIMS

Surgical site infection is a major concern in cleft soft palate. Knowledge of the type, number and antimicrobial resistance of pathogens present preoperatively contribute to treatment success. The aim of this study is to determine whether or not the microbial contamination (diversity) preoperatively has changed since 2015.

METHODS AND RESULTS

Swabs were taken from the surgical site in 103 consecutive patients who presented for primary repair of the soft palate cleft. These were sent for microscopy, culture and sensitivity testing. Swabs were taken before disinfecting the site. Results were tabled and compared with two previous studies from the same facility. Out of 103 patients, 100 patients showed positive cultures with 42 different pathogenic micro-organisms identified. Most dominant pathogen was Klebsiella pneumoniae, 45.6%, increased by 28% from the previous two studies, with 93.6% of these pathogens resistant to one or more antimicrobials. Most of the other identified pathogens showed an alarming increase in occurrence, with a wide resistance to antimicrobials.

CONCLUSIONS

The increase in number and diversity of microbial contamination as well as their resistance to antimicrobials is a real concern. Ways of preventing postoperative infection in a natural way need to be explored.

SIGNIFICANCE

Surgeons need to be aware of constant changes in micro-organisms.

Dysbiosis in the Salivary Microbiome Associated with IgA Nephropathy-‍A‍ ‍Japanese Cohort Study

IgA nephropathy is one of the leading causes of chronic kidney disease in Japan. Since the origin and mechanisms by which IgA nephropathy develops currently remain unclear, a confirmed disease diagnosis is currently only possible by highly invasive renal biopsy. With the background of the salivary microbiome as a rich source of biomarkers for systemic diseases, we herein primarily aimed to investigate the salivary microbiome as a tool for the non-invasive diagnosis of IgA nephropathy. In a comparison of salivary microbiome profiles using 16S rRNA amplicon sequencing, significant differences were observed in microbial diversity and richness between IgA nephropathy patients and healthy controls. Furthermore, recent studies reported that patients with IgA nephropathy are more likely to develop inflammatory bowel diseases and that chronic inflammation of the tonsils triggered the recurrence of IgA nephropathy. Therefore, we compared the salivary microbiome of IgA nephropathy patients with chronic tonsillitis and ulcerative colitis patients. By combining the genera selected by the random forest algorithm, we were able to distinguish IgA nephropathy from healthy controls with an area under the curve (AUC) of 0.90, from the ulcerative colitis group with AUC of 0.88, and from the chronic tonsillitis group with AUC of 0.70. Additionally, the genus Neisseria was common among the selected genera that facilitated the separation of the IgA nephropathy group from healthy controls and the chronic tonsillitis group. The present results indicate the potential of the salivary microbiome as a biomarker for the non-invasive diagnosis of IgA nephropathy.

Haemophilus influenzae: using comparative genomics to accurately identify a highly recombinogenic human pathogen

Background

Haemophilus influenzae is an opportunistic bacterial pathogen that exclusively colonises humans and is associated with both acute and chronic disease. Despite its clinical significance, accurate identification of H. influenzae is a non-trivial endeavour. H. haemolyticus can be misidentified as H. influenzae from clinical specimens using selective culturing methods, reflecting both the shared environmental niche and phenotypic similarities of these species. On the molecular level, frequent genetic exchange amongst Haemophilus spp. has confounded accurate identification of H. influenzae, leading to both false-positive and false-negative results with existing speciation assays.

Results

Whole-genome single-nucleotide polymorphism data from 246 closely related global Haemophilus isolates, including 107 Australian isolate genomes generated in this study, were used to construct a whole-genome phylogeny. Based on this phylogeny, H. influenzae could be differentiated from closely related species. Next, a H. influenzae-specific locus, fucP, was identified, and a novel TaqMan real-time PCR assay targeting fucP was designed. PCR specificity screening across a panel of clinically relevant species, coupled with in silico analysis of all species within the order Pasteurellales, demonstrated that the fucP assay was 100 % specific for H. influenzae; all other examined species failed to amplify.

Conclusions

This study is the first of its kind to use large-scale comparative genomic analysis of Haemophilus spp. to accurately delineate H. influenzae and to identify a species-specific molecular signature for this species. The fucP assay outperforms existing H. influenzae targets, most of which were identified prior to the next-generation genomics era and thus lack validation across a large number of Haemophilus spp. We recommend use of the fucP assay in clinical and research laboratories for the most accurate detection and diagnosis of H. influenzae infection and colonisation.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1857-x) contains supplementary material, which is available to authorized users.